Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Aflatoxins produced by the Aspergillus species are highly toxic, carcinogenic, and cause severe contamination to food sources, leading to serious health consequences. Contaminations by aflatoxins have been reported in food and feed, such as groundnuts, millet, sesame seeds, maize, wheat, rice, fig, spices and cocoa due to fungal infection during pre- and post-harvest conditions. Besides these food products, commercial products like peanut butter, cooking oil and cosmetics have also been reported to be contaminated by aflatoxins. Even a low concentration of aflatoxins is hazardous for human and livestock. The identification and quantification of aflatoxins in food and feed is a major challenge to guarantee food safety. Therefore, developing feasible, sensitive and robust analytical methods is paramount for the identification and quantification of aflatoxins present in low concentrations in food and feed. There are various chromatographic and sensor-based methods used for the detection of aflatoxins. The current review provides insight into the sources of contamination, occurrence, detection techniques, and masked mycotoxin, in addition to management strategies of aflatoxins to ensure food safety and security.

/pdf/aflatoxins-in-food-and-feed-an-overview-on-prevalence-1sh8oa6frc.pdf

Aflatoxins in Food and Feed: An Overview on Prevalence, Detection and Control Strategies.

Biogeochemistry of soil organic matter in agroecosystems & environmental implications.

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

/pdf/current-state-of-hyperspectral-remote-sensing-for-early-26u512cv.pdf

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

The rapid and efficient selection of eligible hybrid progeny is an important step in cross breeding. However, selecting hybrid offspring that meets specific requirements can be time consuming and expensive. Here, near-infrared hyperspectral imaging technology combined with deep learning was applied to classifying hybrid seeds. The hyperspectral images in the range of 975–1648 nm of a total of 6136 hybrid okra seeds and 4128 hybrid loofah seeds, which both contained six varieties, were collected. A partial least squares discriminant analysis, support vector machine and deep convolutional neural network (DCNN) were used to establish discriminant analysis models, and their performances were compared among the different hybrid seed varieties. The discriminant analysis model based on the DCNN was the most stable and had the highest classification accuracy, greater than 95%. The values of features in the last layer of the DCNN were visualized using t-distribution stochastic neighbor embedding. The discriminant analysis model based on the DCNN had the advantages of reducing the labor burden and time required in cross breeding-based progeny selection, which will accelerate the progress of related research.

Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

In karst landscapes stony soils have little water holding capacity; the rational use of water for irrigation therefore plays an important management role. Because the water holding capacity is not homogenous, precision agriculture approaches would enable better management decisions. This research was carried out in an experimental vineyard grown in an artificially transformed karst terrain in Dalmatia, Croatia. The experimental design included four water treatments in three replicates: (1) fully irrigated, based on 100% crop evapotranspiration (ETc) application (N100); (2 and (3) deficit irrigation, based on 75% and 50% ETc applications (N75 and N50, respectively); and (4) non-irrigated (N0). Hyperspectral images of grapevines were taken in the summer of 2016 using two spectral-radiance (W sr−1 m−2) calibrated cameras, covering wavelengths from 409 to 988 nm and 950 to 2509 nm. The four treatments were grouped into a new set consisting of: (1) drought (N0); and (2) irrigated (the remaining three treatments: N100, N75, and N50). The images were analyzed using Partial Least Squares-Discriminant Analysis (PLS-DA), and treatments were classified using PLS-Single Vector Machines (PLS-SVM). PLS-SVM demonstrated the capability to determine levels of grapevine drought or irrigated treatments with an accuracy of more than 97%. PLS-DA identified relevant wavelengths, which were linked to O–H, C–H, and N–H stretches in water, carbohydrates and proteins. The study presents the applicability of hyperspectral imaging for drought stress assessment in grapevines, even though temporal variability needs to be taken into account for early detection.

/pdf/hyperspectral-remote-sensing-of-grapevine-drought-stress-4q86hjjg4p.pdf

Hyperspectral remote sensing of grapevine drought stress

Near-infrared (NIR) hyperspectral imaging was explored as a rapid and non-destructive method of investigating seed quality parameters such as seed viability and variation in tomato seed lots. The seed lots differed with year of production and variety. Four tomato varieties: Cal J, Monprecus, NCL and Chiuri from 2013, 2014 and 2015 were used in the study. The extracted NIR hyperspectral data from 975 to 2500 nm were analysed by principal component analysis (PCA) and partial least squares- discriminant analysis (PLS-DA). No distinct patterns of separation between viable and non-viable tomato seeds were revealed by the PCA. Our findings showed a pattern of separation in the tomato seed lots due to production years and varieties. The PLS-DA showed the ability to predict with ∼100 percent accuracy for varietal class membership when only the seeds of a single harvest year were included in the model. The accuracy from PLS-DA on pooled samples (all seeds from all varieties) predicted varietal class membership in the range from 34 to 88 percent. High variation in the seed lots could have caused high variation in the predicted varietal class membership. The NIR regions with chemical information from C H, N H and O H had influence on the PCA and PLS-DA models. The study presents the prospects of using NIR hyperspectral imaging in varietal identification studies of tomato seeds though we recommend a thorough validation of models.

Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis

Hyperspectral imagery as a supporting tool in precision irrigation of karst landscapes

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Hyperspectral remote sensing of grapevine drought stress

Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis

Hyperspectral imagery as a supporting tool in precision irrigation of karst landscapes